BibTeX
@INCOLLECTION{
         Lotz2012HAD,
       title = "Hierarchical Algorithmic Differentiation A Case Study",
       doi = "10.1007/978-3-642-30023-3_17",
       author = "Johannes Lotz and Uwe Naumann and J{\"o}rn Ungermann",
       abstract = "This case study in Algorithmic Differentiation (AD) discusses the semi-automatic
         generation of an adjoint simulation code in the context of an inverse atmospheric remote sensing
         problem. In-depth structural and performance analyses allow for the run time factor between the
         adjoint generated by overloading in C++ and the original forward simulation to be reduced to 3. 5.
         The dense Jacobian matrix of the underlying problem is computed at the same cost. This is achieved
         by a hierarchical AD using adjoint mode locally for preaccumulation and by exploiting interface
         contraction. For the given application this approach yields a speed-up over black-box tangent-linear
         and adjoint mode of more than 170. Furthermore, the memory consumption is reduced by a factor of
         1,000 compared to applying black-box adjoint mode.",
       pages = "187--196",
       crossref = "Forth2012RAi",
       booktitle = "Recent Advances in Algorithmic Differentiation",
       series = "Lecture Notes in Computational Science and Engineering",
       publisher = "Springer",
       address = "Berlin",
       volume = "87",
       editor = "Shaun Forth and Paul Hovland and Eric Phipps and Jean Utke and Andrea Walther",
       isbn = "978-3-540-68935-5",
       issn = "1439-7358",
       year = "2012",
       ad_theotech = "Hierarchical Approach"
}